We also use another baseline similar in style to VGG [1]}, i.e., our model consists of a series of convolutions and ReLU non-linearities. More-specifically our architecture consists of 6 convolution layers, each of which operates on 3D input. Table REF  lists the different parameters for each of these convolution layers. The output of each convolution layer is passed through ReLU non-linearity. The final output of the convolution is flattened to a 512 dimensional vector. This vector is then passed through multiple fully-connected layers with output size \([256, 64, 1]\) .
